Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations3640
Missing cells6027
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory924.1 KiB
Average record size in memory260.0 B

Variable types

Numeric15
Text1
Categorical2

Alerts

HOSPITALES has constant value "0.0"Constant
ALUMNOS is highly overall correlated with EMPRESAS and 4 other fieldsHigh correlation
COD_MUNICIPIO is highly overall correlated with FARMACIASHigh correlation
CONSULTORIOS is highly overall correlated with FARMACIAS and 1 other fieldsHigh correlation
EMPRESAS is highly overall correlated with ALUMNOS and 6 other fieldsHigh correlation
ESCUELAS is highly overall correlated with ALUMNOS and 4 other fieldsHigh correlation
FARMACIAS is highly overall correlated with ALUMNOS and 6 other fieldsHigh correlation
POBLACION_TOTAL is highly overall correlated with ALUMNOS and 6 other fieldsHigh correlation
PROP_POBLACION_EXTRANJERA is highly overall correlated with EMPRESAS and 1 other fieldsHigh correlation
SERVICIOS_SANIDAD is highly overall correlated with CONSULTORIOS and 3 other fieldsHigh correlation
SERVICIOS_TURISTICOS is highly overall correlated with ALUMNOS and 3 other fieldsHigh correlation
TASA_DEMANDA is highly overall correlated with TASA_PAROHigh correlation
TASA_PARO is highly overall correlated with TASA_DEMANDAHigh correlation
FARMACIAS is highly imbalanced (55.9%)Imbalance
RATIO_DEMANDA_PARO has 1251 (34.4%) missing valuesMissing
SERVICIOS_SANIDAD has 1456 (40.0%) missing valuesMissing
CONSULTORIOS has 546 (15.0%) missing valuesMissing
FARMACIAS has 1456 (40.0%) missing valuesMissing
EMPRESAS has 1274 (35.0%) missing valuesMissing
TASA_DEMANDA has 1056 (29.0%) zerosZeros
TASA_PARO has 1246 (34.2%) zerosZeros
SERVICIOS_TURISTICOS has 1691 (46.5%) zerosZeros
SERVICIOS_SANIDAD has 140 (3.8%) zerosZeros
CONSULTORIOS has 244 (6.7%) zerosZeros
ESCUELAS has 3229 (88.7%) zerosZeros
ALUMNOS has 3243 (89.1%) zerosZeros
EMPRESAS has 1354 (37.2%) zerosZeros
PROP_POBLACION_EXTRANJERA has 1497 (41.1%) zerosZeros

Reproduction

Analysis started2025-12-22 17:36:01.727649
Analysis finished2025-12-22 17:37:14.005925
Duration1 minute and 12.28 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

FECHA
Real number (ℝ)

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.5
Minimum2005
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:14.196577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005.95
Q12009.75
median2014.5
Q32019.25
95-th percentile2023.05
Maximum2024
Range19
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.7670735
Coefficient of variation (CV)0.0028627816
Kurtosis-1.2060231
Mean2014.5
Median Absolute Deviation (MAD)5
Skewness0
Sum7332780
Variance33.259137
MonotonicityIncreasing
2025-12-22T18:37:14.547377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2005182
 
5.0%
2006182
 
5.0%
2023182
 
5.0%
2022182
 
5.0%
2021182
 
5.0%
2020182
 
5.0%
2019182
 
5.0%
2018182
 
5.0%
2017182
 
5.0%
2016182
 
5.0%
Other values (10)1820
50.0%
ValueCountFrequency (%)
2005182
5.0%
2006182
5.0%
2007182
5.0%
2008182
5.0%
2009182
5.0%
2010182
5.0%
2011182
5.0%
2012182
5.0%
2013182
5.0%
2014182
5.0%
ValueCountFrequency (%)
2024182
5.0%
2023182
5.0%
2022182
5.0%
2021182
5.0%
2020182
5.0%
2019182
5.0%
2018182
5.0%
2017182
5.0%
2016182
5.0%
2015182
5.0%

COD_MUNICIPIO
Real number (ℝ)

High correlation 

Distinct182
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42107.401
Minimum42001
Maximum42219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:14.922989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42001
5-th percentile42012
Q142050
median42105.5
Q342164
95-th percentile42208
Maximum42219
Range218
Interquartile range (IQR)114

Descriptive statistics

Standard deviation64.707672
Coefficient of variation (CV)0.0015367292
Kurtosis-1.2727654
Mean42107.401
Median Absolute Deviation (MAD)57
Skewness0.07255156
Sum1.5327094 × 108
Variance4187.0828
MonotonicityNot monotonic
2025-12-22T18:37:15.271623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4200120
 
0.5%
4213520
 
0.5%
4214020
 
0.5%
4214120
 
0.5%
4214220
 
0.5%
4214420
 
0.5%
4214520
 
0.5%
4214820
 
0.5%
4214920
 
0.5%
4215120
 
0.5%
Other values (172)3440
94.5%
ValueCountFrequency (%)
4200120
0.5%
4200320
0.5%
4200420
0.5%
4200620
0.5%
4200720
0.5%
4200820
0.5%
4200920
0.5%
4201020
0.5%
4201120
0.5%
4201220
0.5%
ValueCountFrequency (%)
4221920
0.5%
4221820
0.5%
4221720
0.5%
4221620
0.5%
4221520
0.5%
4221320
0.5%
4221220
0.5%
4221120
0.5%
4220920
0.5%
4220820
0.5%
Distinct182
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size277.2 KiB
2025-12-22T18:37:15.777415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length34
Median length26
Mean length18.434066
Min length10

Characters and Unicode

Total characters67100
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42001 ABEJAR
2nd row42003 ADRADAS
3rd row42004 AGREDA
4th row42006 ALCONABA
5th row42007 ALCUBILLA DE AVELLANEDA
ValueCountFrequency (%)
de1180
 
11.0%
la220
 
2.1%
del200
 
1.9%
soria180
 
1.7%
san140
 
1.3%
sierra120
 
1.1%
almazan100
 
0.9%
las80
 
0.7%
gormaz80
 
0.7%
duero80
 
0.7%
Other values (389)8300
77.7%
2025-12-22T18:37:16.834747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A7140
 
10.6%
7040
 
10.5%
E5140
 
7.7%
24680
 
7.0%
44300
 
6.4%
L3680
 
5.5%
R3180
 
4.7%
O3140
 
4.7%
02520
 
3.8%
D2460
 
3.7%
Other values (29)23820
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)67100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A7140
 
10.6%
7040
 
10.5%
E5140
 
7.7%
24680
 
7.0%
44300
 
6.4%
L3680
 
5.5%
R3180
 
4.7%
O3140
 
4.7%
02520
 
3.8%
D2460
 
3.7%
Other values (29)23820
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)67100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A7140
 
10.6%
7040
 
10.5%
E5140
 
7.7%
24680
 
7.0%
44300
 
6.4%
L3680
 
5.5%
R3180
 
4.7%
O3140
 
4.7%
02520
 
3.8%
D2460
 
3.7%
Other values (29)23820
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)67100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A7140
 
10.6%
7040
 
10.5%
E5140
 
7.7%
24680
 
7.0%
44300
 
6.4%
L3680
 
5.5%
R3180
 
4.7%
O3140
 
4.7%
02520
 
3.8%
D2460
 
3.7%
Other values (29)23820
35.5%

POBLACION_TOTAL
Real number (ℝ)

High correlation 

Distinct732
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.40604
Minimum6
Maximum6006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:17.231826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile16
Q136
median75
Q3177
95-th percentile1364.95
Maximum6006
Range6000
Interquartile range (IQR)141

Descriptive statistics

Standard deviation750.33578
Coefficient of variation (CV)2.6107168
Kurtosis26.71955
Mean287.40604
Median Absolute Deviation (MAD)47
Skewness4.9275176
Sum1046158
Variance563003.78
MonotonicityNot monotonic
2025-12-22T18:37:17.630035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3348
 
1.3%
2647
 
1.3%
2047
 
1.3%
2543
 
1.2%
2942
 
1.2%
2842
 
1.2%
2742
 
1.2%
2441
 
1.1%
3238
 
1.0%
3638
 
1.0%
Other values (722)3212
88.2%
ValueCountFrequency (%)
62
 
0.1%
77
 
0.2%
819
0.5%
927
0.7%
1013
0.4%
1110
 
0.3%
1212
 
0.3%
1330
0.8%
1431
0.9%
1524
0.7%
ValueCountFrequency (%)
60061
< 0.1%
60051
< 0.1%
59841
< 0.1%
59651
< 0.1%
58611
< 0.1%
58431
< 0.1%
58231
< 0.1%
57441
< 0.1%
57341
< 0.1%
57272
0.1%

PROP_ENVEJECIMIENTO
Real number (ℝ)

Distinct1837
Distinct (%)50.6%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.41149462
Minimum0.047619048
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:18.019790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.047619048
5-th percentile0.21920923
Q10.328063
median0.40757614
Q30.49122807
95-th percentile0.6171505
Maximum0.8
Range0.75238095
Interquartile range (IQR)0.16316507

Descriptive statistics

Standard deviation0.12157399
Coefficient of variation (CV)0.2954449
Kurtosis-0.047848981
Mean0.41149462
Median Absolute Deviation (MAD)0.081785561
Skewness0.17575124
Sum1495.3714
Variance0.014780234
MonotonicityNot monotonic
2025-12-22T18:37:18.506952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.592
 
2.5%
0.333333333353
 
1.5%
0.440
 
1.1%
0.428571428630
 
0.8%
0.454545454529
 
0.8%
0.37526
 
0.7%
0.666666666724
 
0.7%
0.444444444423
 
0.6%
0.363636363622
 
0.6%
0.2519
 
0.5%
Other values (1827)3276
90.0%
ValueCountFrequency (%)
0.047619047621
< 0.1%
0.051
< 0.1%
0.06251
< 0.1%
0.084065244671
< 0.1%
0.085346215781
< 0.1%
0.086430423511
< 0.1%
0.087465833661
< 0.1%
0.088778729161
< 0.1%
0.088924176261
< 0.1%
0.090732087231
< 0.1%
ValueCountFrequency (%)
0.81
 
< 0.1%
0.77777777786
0.2%
0.77419354841
 
< 0.1%
0.76923076923
0.1%
0.76470588242
 
0.1%
0.762
 
0.1%
0.75862068971
 
< 0.1%
0.753
0.1%
0.74074074071
 
< 0.1%
0.73913043481
 
< 0.1%

TASA_DEMANDA
Real number (ℝ)

High correlation  Zeros 

Distinct1009
Distinct (%)27.8%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.8127935
Minimum0
Maximum112.19512
Zeros1056
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:18.897249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.9166435
Q39.0909091
95-th percentile16.25
Maximum112.19512
Range112.19512
Interquartile range (IQR)9.0909091

Descriptive statistics

Standard deviation5.8726128
Coefficient of variation (CV)1.010291
Kurtosis30.272851
Mean5.8127935
Median Absolute Deviation (MAD)4.9166435
Skewness2.5626818
Sum21123.692
Variance34.487581
MonotonicityNot monotonic
2025-12-22T18:37:19.246005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01056
29.0%
12.542
 
1.2%
1038
 
1.0%
6.2537
 
1.0%
11.1111111135
 
1.0%
7.14285714334
 
0.9%
5.55555555634
 
0.9%
5.26315789532
 
0.9%
9.09090909130
 
0.8%
8.33333333329
 
0.8%
Other values (999)2267
62.3%
ValueCountFrequency (%)
01056
29.0%
0.58823529411
 
< 0.1%
0.64516129031
 
< 0.1%
0.67114093961
 
< 0.1%
0.68965517241
 
< 0.1%
0.80645161291
 
< 0.1%
0.81300813011
 
< 0.1%
0.81967213112
 
0.1%
0.87719298252
 
0.1%
0.89285714291
 
< 0.1%
ValueCountFrequency (%)
112.1951221
< 0.1%
33.333333332
0.1%
31.653225811
< 0.1%
31.42292491
< 0.1%
31.251
< 0.1%
30.701754391
< 0.1%
30.555555561
< 0.1%
301
< 0.1%
29.411764711
< 0.1%
28.571428572
0.1%

TASA_PARO
Real number (ℝ)

High correlation  Zeros 

Distinct940
Distinct (%)25.9%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.2366196
Minimum0
Maximum33.333333
Zeros1246
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:19.557856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5087719
Q36.6969705
95-th percentile12.5
Maximum33.333333
Range33.333333
Interquartile range (IQR)6.6969705

Descriptive statistics

Standard deviation4.4680381
Coefficient of variation (CV)1.0546234
Kurtosis2.8725439
Mean4.2366196
Median Absolute Deviation (MAD)3.5087719
Skewness1.3544113
Sum15395.875
Variance19.963365
MonotonicityNot monotonic
2025-12-22T18:37:19.861716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01246
34.2%
5.55555555634
 
0.9%
7.14285714332
 
0.9%
4.54545454532
 
0.9%
11.1111111131
 
0.9%
3.84615384630
 
0.8%
3.57142857129
 
0.8%
6.2529
 
0.8%
4.16666666728
 
0.8%
1028
 
0.8%
Other values (930)2115
58.1%
ValueCountFrequency (%)
01246
34.2%
0.54945054951
 
< 0.1%
0.62111801241
 
< 0.1%
0.64516129031
 
< 0.1%
0.69930069931
 
< 0.1%
0.80645161291
 
< 0.1%
0.81300813011
 
< 0.1%
0.84745762711
 
< 0.1%
0.87719298251
 
< 0.1%
0.89285714292
 
0.1%
ValueCountFrequency (%)
33.333333331
 
< 0.1%
301
 
< 0.1%
29.82456141
 
< 0.1%
29.411764711
 
< 0.1%
28.571428571
 
< 0.1%
28.048780491
 
< 0.1%
26.814516131
 
< 0.1%
255
0.1%
24.545454551
 
< 0.1%
23.076923081
 
< 0.1%

RATIO_DEMANDA_PARO
Real number (ℝ)

Missing 

Distinct362
Distinct (%)15.2%
Missing1251
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean1.3807827
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:20.133877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.2439024
Q31.5
95-th percentile2.3333333
Maximum7
Range6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.51147281
Coefficient of variation (CV)0.37042238
Kurtosis13.705564
Mean1.3807827
Median Absolute Deviation (MAD)0.24390244
Skewness2.7175937
Sum3298.6898
Variance0.26160443
MonotonicityNot monotonic
2025-12-22T18:37:20.450917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1891
24.5%
2192
 
5.3%
1.5156
 
4.3%
1.333333333104
 
2.9%
1.2577
 
2.1%
1.258
 
1.6%
357
 
1.6%
1.66666666746
 
1.3%
1.432
 
0.9%
1.16666666731
 
0.9%
Other values (352)745
20.5%
(Missing)1251
34.4%
ValueCountFrequency (%)
1891
24.5%
1.0294117651
 
< 0.1%
1.0370370371
 
< 0.1%
1.0540540541
 
< 0.1%
1.0555555562
 
0.1%
1.0666666673
 
0.1%
1.0714285712
 
0.1%
1.0751
 
< 0.1%
1.0763358781
 
< 0.1%
1.0769230772
 
0.1%
ValueCountFrequency (%)
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
4.51
 
< 0.1%
49
 
0.2%
3.51
 
< 0.1%
3.251
 
< 0.1%
3.2229729731
 
< 0.1%
357
1.6%
2.9090909091
 
< 0.1%

SERVICIOS_TURISTICOS
Real number (ℝ)

High correlation  Zeros 

Distinct41
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5851648
Minimum0
Maximum40
Zeros1691
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:20.774472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile19
Maximum40
Range40
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.5231235
Coefficient of variation (CV)1.8194766
Kurtosis7.6660786
Mean3.5851648
Median Absolute Deviation (MAD)1
Skewness2.7018003
Sum13050
Variance42.55114
MonotonicityNot monotonic
2025-12-22T18:37:21.366059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
01691
46.5%
1392
 
10.8%
2314
 
8.6%
3287
 
7.9%
4179
 
4.9%
5118
 
3.2%
671
 
2.0%
765
 
1.8%
856
 
1.5%
1345
 
1.2%
Other values (31)422
 
11.6%
ValueCountFrequency (%)
01691
46.5%
1392
 
10.8%
2314
 
8.6%
3287
 
7.9%
4179
 
4.9%
5118
 
3.2%
671
 
2.0%
765
 
1.8%
856
 
1.5%
924
 
0.7%
ValueCountFrequency (%)
401
 
< 0.1%
395
0.1%
383
 
0.1%
372
 
0.1%
368
0.2%
354
0.1%
341
 
< 0.1%
331
 
< 0.1%
322
 
0.1%
313
 
0.1%

SERVICIOS_SANIDAD
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct14
Distinct (%)0.6%
Missing1456
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean2.139652
Minimum0
Maximum18
Zeros140
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:21.633960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.404798
Coefficient of variation (CV)1.1239202
Kurtosis14.686112
Mean2.139652
Median Absolute Deviation (MAD)1
Skewness3.389628
Sum4673
Variance5.7830537
MonotonicityNot monotonic
2025-12-22T18:37:21.880844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11090
29.9%
2448
 
12.3%
3168
 
4.6%
4141
 
3.9%
0140
 
3.8%
566
 
1.8%
825
 
0.7%
1324
 
0.7%
623
 
0.6%
920
 
0.5%
Other values (4)39
 
1.1%
(Missing)1456
40.0%
ValueCountFrequency (%)
0140
 
3.8%
11090
29.9%
2448
12.3%
3168
 
4.6%
4141
 
3.9%
566
 
1.8%
623
 
0.6%
715
 
0.4%
825
 
0.7%
920
 
0.5%
ValueCountFrequency (%)
1812
 
0.3%
1324
 
0.7%
129
 
0.2%
103
 
0.1%
920
 
0.5%
825
 
0.7%
715
 
0.4%
623
 
0.6%
566
1.8%
4141
3.9%

CONSULTORIOS
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct14
Distinct (%)0.5%
Missing546
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1.865223
Minimum0
Maximum17
Zeros244
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:22.117269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1306197
Coefficient of variation (CV)1.1422868
Kurtosis17.685137
Mean1.865223
Median Absolute Deviation (MAD)0
Skewness3.6570358
Sum5771
Variance4.5395403
MonotonicityNot monotonic
2025-12-22T18:37:22.352211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11793
49.3%
2423
 
11.6%
3275
 
7.6%
0244
 
6.7%
4158
 
4.3%
549
 
1.3%
733
 
0.9%
828
 
0.8%
623
 
0.6%
1017
 
0.5%
Other values (4)51
 
1.4%
(Missing)546
 
15.0%
ValueCountFrequency (%)
0244
 
6.7%
11793
49.3%
2423
 
11.6%
3275
 
7.6%
4158
 
4.3%
549
 
1.3%
623
 
0.6%
733
 
0.9%
828
 
0.8%
96
 
0.2%
ValueCountFrequency (%)
1717
 
0.5%
1216
 
0.4%
1112
 
0.3%
1017
 
0.5%
96
 
0.2%
828
 
0.8%
733
 
0.9%
623
 
0.6%
549
 
1.3%
4158
4.3%

FARMACIAS
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.2%
Missing1456
Missing (%)40.0%
Memory size219.0 KiB
0.0
1645 
1.0
515 
4.0
 
12
3.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6552
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01645
45.2%
1.0515
 
14.1%
4.012
 
0.3%
3.012
 
0.3%
(Missing)1456
40.0%

Length

2025-12-22T18:37:22.603118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-22T18:37:22.893090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01645
75.3%
1.0515
 
23.6%
4.012
 
0.5%
3.012
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03829
58.4%
.2184
33.3%
1515
 
7.9%
412
 
0.2%
312
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03829
58.4%
.2184
33.3%
1515
 
7.9%
412
 
0.2%
312
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03829
58.4%
.2184
33.3%
1515
 
7.9%
412
 
0.2%
312
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03829
58.4%
.2184
33.3%
1515
 
7.9%
412
 
0.2%
312
 
0.2%

HOSPITALES
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.3 KiB
0.0
3640 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03640
100.0%

Length

2025-12-22T18:37:23.189017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-22T18:37:23.422056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03640
100.0%

Most occurring characters

ValueCountFrequency (%)
07280
66.7%
.3640
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07280
66.7%
.3640
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07280
66.7%
.3640
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07280
66.7%
.3640
33.3%

ESCUELAS
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.2%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.2698895
Minimum0
Maximum8
Zeros3229
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:23.616345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94987935
Coefficient of variation (CV)3.519512
Kurtosis25.070061
Mean0.2698895
Median Absolute Deviation (MAD)0
Skewness4.5994404
Sum977
Variance0.90227078
MonotonicityNot monotonic
2025-12-22T18:37:23.834693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
03229
88.7%
1139
 
3.8%
287
 
2.4%
384
 
2.3%
447
 
1.3%
813
 
0.4%
512
 
0.3%
76
 
0.2%
63
 
0.1%
(Missing)20
 
0.5%
ValueCountFrequency (%)
03229
88.7%
1139
 
3.8%
287
 
2.4%
384
 
2.3%
447
 
1.3%
512
 
0.3%
63
 
0.1%
76
 
0.2%
813
 
0.4%
ValueCountFrequency (%)
813
 
0.4%
76
 
0.2%
63
 
0.1%
512
 
0.3%
447
 
1.3%
384
 
2.3%
287
 
2.4%
1139
 
3.8%
03229
88.7%

ALUMNOS
Real number (ℝ)

High correlation  Zeros 

Distinct277
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.279396
Minimum0
Maximum1662
Zeros3243
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:24.107855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile246.05
Maximum1662
Range1662
Interquartile range (IQR)0

Descriptive statistics

Standard deviation157.789
Coefficient of variation (CV)4.4725539
Kurtosis43.185157
Mean35.279396
Median Absolute Deviation (MAD)0
Skewness6.1070453
Sum128417
Variance24897.368
MonotonicityNot monotonic
2025-12-22T18:37:24.402641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03243
89.1%
397
 
0.2%
577
 
0.2%
355
 
0.1%
295
 
0.1%
455
 
0.1%
345
 
0.1%
424
 
0.1%
264
 
0.1%
434
 
0.1%
Other values (267)351
 
9.6%
ValueCountFrequency (%)
03243
89.1%
72
 
0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
142
 
0.1%
151
 
< 0.1%
162
 
0.1%
ValueCountFrequency (%)
16621
< 0.1%
16071
< 0.1%
15621
< 0.1%
15271
< 0.1%
15091
< 0.1%
15071
< 0.1%
14991
< 0.1%
14971
< 0.1%
14771
< 0.1%
14571
< 0.1%

EMPRESAS
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct140
Distinct (%)5.9%
Missing1274
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean15.714708
Minimum0
Maximum393
Zeros1354
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:24.740794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile91.75
Maximum393
Range393
Interquartile range (IQR)9

Descriptive statistics

Standard deviation46.946015
Coefficient of variation (CV)2.9873933
Kurtosis26.041589
Mean15.714708
Median Absolute Deviation (MAD)0
Skewness4.7976962
Sum37181
Variance2203.9283
MonotonicityNot monotonic
2025-12-22T18:37:25.055563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01354
37.2%
4143
 
3.9%
5105
 
2.9%
666
 
1.8%
1050
 
1.4%
843
 
1.2%
743
 
1.2%
942
 
1.2%
1141
 
1.1%
1438
 
1.0%
Other values (130)441
 
12.1%
(Missing)1274
35.0%
ValueCountFrequency (%)
01354
37.2%
4143
 
3.9%
5105
 
2.9%
666
 
1.8%
743
 
1.2%
843
 
1.2%
942
 
1.2%
1050
 
1.4%
1141
 
1.1%
1230
 
0.8%
ValueCountFrequency (%)
3931
< 0.1%
3841
< 0.1%
3831
< 0.1%
3801
< 0.1%
3771
< 0.1%
3691
< 0.1%
3671
< 0.1%
3661
< 0.1%
3641
< 0.1%
3591
< 0.1%

PROP_POBLACION_EXTRANJERA
Real number (ℝ)

High correlation  Zeros 

Distinct1269
Distinct (%)34.9%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.037668568
Minimum0
Maximum0.38120104
Zeros1497
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:25.403401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.016143511
Q30.054294958
95-th percentile0.15532797
Maximum0.38120104
Range0.38120104
Interquartile range (IQR)0.054294958

Descriptive statistics

Standard deviation0.052830606
Coefficient of variation (CV)1.4025117
Kurtosis3.5348117
Mean0.037668568
Median Absolute Deviation (MAD)0.016143511
Skewness1.8520579
Sum136.88758
Variance0.002791073
MonotonicityNot monotonic
2025-12-22T18:37:25.744058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01497
41.1%
0.0454545454514
 
0.4%
0.0357142857113
 
0.4%
0.0526315789513
 
0.4%
0.0666666666713
 
0.4%
0.0277777777813
 
0.4%
0.0166666666713
 
0.4%
0.0476190476212
 
0.3%
0.0416666666712
 
0.3%
0.0270270270311
 
0.3%
Other values (1259)2023
55.6%
ValueCountFrequency (%)
01497
41.1%
0.0029498525071
 
< 0.1%
0.0029850746271
 
< 0.1%
0.0030487804881
 
< 0.1%
0.0031251
 
< 0.1%
0.0034722222221
 
< 0.1%
0.0035587188611
 
< 0.1%
0.0037313432841
 
< 0.1%
0.0037735849061
 
< 0.1%
0.0038167938931
 
< 0.1%
ValueCountFrequency (%)
0.38120104441
< 0.1%
0.33421750661
< 0.1%
0.32352941181
< 0.1%
0.31
< 0.1%
0.28318584071
< 0.1%
0.28301886791
< 0.1%
0.27777777781
< 0.1%
0.27272727272
0.1%
0.26213592231
< 0.1%
0.26016260161
< 0.1%

DISTANCIA_CAPITAL
Real number (ℝ)

Distinct168
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.365385
Minimum4
Maximum98.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2025-12-22T18:37:26.126945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15.4
Q132.3
median47.95
Q362.9
95-th percentile82.4
Maximum98.6
Range94.6
Interquartile range (IQR)30.6

Descriptive statistics

Standard deviation20.069817
Coefficient of variation (CV)0.41496241
Kurtosis-0.52260789
Mean48.365385
Median Absolute Deviation (MAD)15.05
Skewness0.15894116
Sum176050
Variance402.79754
MonotonicityNot monotonic
2025-12-22T18:37:26.573636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.960
 
1.6%
27.440
 
1.1%
34.540
 
1.1%
71.240
 
1.1%
44.640
 
1.1%
46.640
 
1.1%
37.840
 
1.1%
52.640
 
1.1%
43.340
 
1.1%
7440
 
1.1%
Other values (158)3220
88.5%
ValueCountFrequency (%)
420
0.5%
8.120
0.5%
8.820
0.5%
12.820
0.5%
1320
0.5%
13.220
0.5%
13.420
0.5%
14.820
0.5%
15.120
0.5%
15.420
0.5%
ValueCountFrequency (%)
98.620
0.5%
95.920
0.5%
93.820
0.5%
92.520
0.5%
88.120
0.5%
87.720
0.5%
87.120
0.5%
86.920
0.5%
83.120
0.5%
82.420
0.5%

Interactions

2025-12-22T18:37:08.557250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:03.082166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:07.612684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:17.395665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:21.248521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:25.630669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:29.934790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:34.132433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:38.363982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:42.440688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:47.239732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:51.427647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:55.718187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:59.816763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:04.387646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:08.795979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:03.309003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:08.095330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:17.641730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:21.506216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:25.860400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:30.264561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:34.351794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:38.670659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:42.688511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:47.567423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:51.724319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:55.939910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:00.048928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:04.659773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:09.280683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:03.882045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:08.788997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:18.157324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:22.060272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:26.489684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:30.927821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:34.974905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:39.286566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:43.543920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:48.108883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:52.455744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:56.436242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:00.708426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:05.191660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:09.503297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:04.106614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:09.269224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:18.405469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:22.361912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:26.729315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:31.148355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:35.289073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:39.507740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:43.814010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:48.321676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:52.676492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:56.656920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:01.255246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:05.427119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:09.748317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:04.356568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:10.133262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:18.685367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:22.640587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:26.997094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:31.401047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:35.525707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:39.774234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:44.093222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:48.573159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:52.943521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:56.883868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:01.524867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:05.676033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:09.937162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:04.591681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:10.751086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:18.918840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:22.957306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:27.239853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:31.652946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:35.727923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:39.970515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:44.370878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:48.789692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:53.191648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:57.082210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:01.766485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:05.923425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:10.217698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:04.843632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:11.370646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:19.138635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:23.295343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:27.483328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:31.856376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:36.046207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:40.193772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:44.639122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:49.025886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:53.442815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:57.302660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:02.038363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:06.246534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:10.713276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:05.412237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:11.894824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:19.383869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:23.551624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:27.697230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:32.055569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:36.349565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:40.422425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:44.945589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:49.296843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:53.675691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:57.528399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:02.349448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:06.508216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:11.019524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:05.771863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:12.567713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:19.624215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:23.776152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:27.986536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:32.251120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:36.680805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:40.681439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:45.270346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:49.547090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:53.923205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:57.766269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:02.655571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:06.787043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:11.314841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:06.030069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:13.483599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:19.824897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:23.977644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:28.292983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:32.482229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:36.969321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:40.903816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:45.567685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:49.786732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:54.149863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:58.005255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:02.960165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:07.062043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:11.555148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:06.285910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:14.122787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:20.031928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:24.417583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:28.552498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:32.716198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:37.200108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:41.140043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:45.885362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:50.026014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:54.384840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:58.341124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:03.202832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:07.364187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:11.741799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:06.560638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:14.896183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:20.267349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:24.647522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:28.820641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:32.975376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:37.441235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:41.393605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:46.157387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:50.268892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:54.668702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:58.677767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:03.456122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:07.637011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:11.900881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:06.839626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:15.603783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:20.488033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:24.870847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:29.052614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:33.213537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:37.668288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:41.634068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:46.444270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:50.536403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:54.954739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:58.955794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:03.696945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:07.840821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:12.119778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:07.121732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:16.090366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:20.778970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:25.140332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:29.326406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:33.458263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:37.922862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:41.881885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:46.711242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:50.835912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:55.257222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:59.272237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:03.932130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:08.069065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:12.344475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:07.409245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:16.645829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:21.039891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:25.408937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:29.672970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:33.930809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:38.151457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:42.208915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:46.975920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:51.164328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:55.515296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:36:59.560235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:04.162094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-12-22T18:37:08.303491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-12-22T18:37:26.948645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ALUMNOSCOD_MUNICIPIOCONSULTORIOSDISTANCIA_CAPITALEMPRESASESCUELASFARMACIASFECHAPOBLACION_TOTALPROP_ENVEJECIMIENTOPROP_POBLACION_EXTRANJERARATIO_DEMANDA_PAROSERVICIOS_SANIDADSERVICIOS_TURISTICOSTASA_DEMANDATASA_PARO
ALUMNOS1.000-0.0690.0790.0400.5760.9930.807-0.0040.528-0.3920.3490.1910.2860.5090.2830.271
COD_MUNICIPIO-0.0691.000-0.1270.025-0.085-0.0750.9510.000-0.061-0.017-0.0140.041-0.1660.0210.0370.027
CONSULTORIOS0.079-0.1271.0000.0700.3070.0780.572-0.0110.441-0.0170.2320.0980.8940.2530.1420.155
DISTANCIA_CAPITAL0.0400.0250.0701.000-0.0140.0370.1780.000-0.0340.286-0.027-0.0860.041-0.0360.0450.056
EMPRESAS0.576-0.0850.307-0.0141.0000.5790.8300.0030.806-0.4420.5060.3380.5500.7500.4490.425
ESCUELAS0.993-0.0750.0780.0370.5791.0000.7950.0080.527-0.3980.3510.1950.2850.5150.2900.277
FARMACIAS0.8070.9510.5720.1780.8300.7951.0000.0000.8650.2310.2730.0770.5610.4580.0760.218
FECHA-0.0040.000-0.0110.0000.0030.0080.0001.000-0.110-0.0500.1220.091-0.0110.0690.1130.075
POBLACION_TOTAL0.528-0.0610.441-0.0340.8060.5270.865-0.1101.000-0.3650.5480.3800.6710.7120.4740.461
PROP_ENVEJECIMIENTO-0.392-0.017-0.0170.286-0.442-0.3980.231-0.050-0.3651.000-0.450-0.252-0.135-0.436-0.328-0.297
PROP_POBLACION_EXTRANJERA0.349-0.0140.232-0.0270.5060.3510.2730.1220.548-0.4501.0000.2570.3920.4700.3990.381
RATIO_DEMANDA_PARO0.1910.0410.098-0.0860.3380.1950.0770.0910.380-0.2520.2571.0000.1940.3300.292-0.133
SERVICIOS_SANIDAD0.286-0.1660.8940.0410.5500.2850.561-0.0110.671-0.1350.3920.1941.0000.4610.2610.265
SERVICIOS_TURISTICOS0.5090.0210.253-0.0360.7500.5150.4580.0690.712-0.4360.4700.3300.4611.0000.4870.458
TASA_DEMANDA0.2830.0370.1420.0450.4490.2900.0760.1130.474-0.3280.3990.2920.2610.4871.0000.919
TASA_PARO0.2710.0270.1550.0560.4250.2770.2180.0750.461-0.2970.381-0.1330.2650.4580.9191.000

Missing values

2025-12-22T18:37:12.679654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-22T18:37:13.293191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-22T18:37:13.744224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FECHACOD_MUNICIPIOMUNICIPIOPOBLACION_TOTALPROP_ENVEJECIMIENTOTASA_DEMANDATASA_PARORATIO_DEMANDA_PAROSERVICIOS_TURISTICOSSERVICIOS_SANIDADCONSULTORIOSFARMACIASHOSPITALESESCUELASALUMNOSEMPRESASPROP_POBLACION_EXTRANJERADISTANCIA_CAPITAL
020054200142001 ABEJAR3920.3061224.1841002.9288701.42857116.0NaNNaNNaN0.0NaN0NaN0.03571427.4
120054200342003 ADRADAS820.4390247.1428577.1428571.0000001.0NaNNaNNaN0.00.00NaN0.00000054.3
220054200442004 AGREDA32160.2661695.1440334.3724281.1764714.0NaNNaNNaN0.02.0461NaN0.04788657.1
320054200642006 ALCONABA1710.2573106.1946903.5398231.7500000.0NaNNaNNaN0.00.00NaN0.01169613.0
420054200742007 ALCUBILLA DE AVELLANEDA1830.5191266.0240966.0240961.0000000.0NaNNaNNaN0.00.00NaN0.01639378.9
520054200842008 ALCUBILLA DE LAS PEÑAS720.5694443.3333333.3333331.0000000.0NaNNaNNaN0.00.00NaN0.00000068.5
620054200942009 ALDEALAFUENTE1240.3629032.5974031.2987012.0000000.0NaNNaNNaN0.00.00NaN0.00000025.1
720054201042010 ALDEALICES280.2500005.2631585.2631581.0000000.0NaNNaNNaN0.00.00NaN0.14285726.8
820054201142011 ALDEALPOZO270.4444440.0000000.000000NaN0.0NaNNaNNaN0.00.00NaN0.00000029.2
920054201242012 ALDEALSEÑOR430.5348840.0000000.000000NaN0.0NaNNaNNaN0.00.00NaN0.00000023.9
FECHACOD_MUNICIPIOMUNICIPIOPOBLACION_TOTALPROP_ENVEJECIMIENTOTASA_DEMANDATASA_PARORATIO_DEMANDA_PAROSERVICIOS_TURISTICOSSERVICIOS_SANIDADCONSULTORIOSFARMACIASHOSPITALESESCUELASALUMNOSEMPRESASPROP_POBLACION_EXTRANJERADISTANCIA_CAPITAL
365020244220842208 VILLAR DEL CAMPO300.40000022.2222225.5555564.0000.01.01.00.00.00.000.00.06666734.5
365120244220942209 VILLAR DEL RIO1560.39240510.3448289.1954021.12511.03.03.00.00.00.007.00.03797544.8
365220244221142211 VILLARES DE SORIA (LOS)750.3376624.2553192.1276602.0002.02.02.00.00.00.007.00.01298722.3
365320244221242212 VILLASAYAS600.3833330.0000000.000000NaN0.02.02.00.00.00.000.00.01666755.2
365420244221342213 VILLASECA DE ARCIEL240.5416670.0000000.000000NaN0.01.01.00.00.00.000.00.00000045.0
365520244221542215 VINUESA8330.2953189.4488196.2992131.50039.01.01.00.00.03.030377.00.05282133.0
365620244221642216 VIZMANOS300.4000000.0000000.000000NaN0.01.01.00.00.00.000.00.03333340.5
365720244221742217 VOZMEDIANO330.3636369.5238109.5238101.0002.01.01.00.00.00.000.00.03030365.8
365820244221842218 YANGUAS1060.23584910.7692313.0769233.5008.01.01.00.00.00.0010.00.28301948.3
365920244221942219 YELO370.4864860.0000000.000000NaN0.01.01.00.00.00.000.00.02702774.0